Overview

Dataset statistics

Number of variables45
Number of observations14555
Missing cells0
Missing cells (%)0.0%
Duplicate rows221
Duplicate rows (%)1.5%
Total size in memory5.0 MiB
Average record size in memory360.0 B

Variable types

BOOL28
CAT9
NUM8

Reproduction

Analysis started2020-08-12 15:50:44.419673
Analysis finished2020-08-12 15:51:11.708179
Duration27.29 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 221 (1.5%) duplicate rows Duplicates
straße has a high cardinality: 338 distinct values High cardinality
ablösevereinbarung has 12613 (86.7%) zeros Zeros
dauer has 14107 (96.9%) zeros Zeros

Variables

PLZ
Categorical

Distinct count50
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
60486
 
723
60385
 
701
60326
 
674
60318
 
648
60316
 
642
Other values (45)
11167
ValueCountFrequency (%) 
604867235.0%
 
603857014.8%
 
603266744.6%
 
603186484.5%
 
603166424.4%
 
604876144.2%
 
605946004.1%
 
603225293.6%
 
605285163.5%
 
603275113.5%
 
Other values (40)839757.7%
 
2020-08-12T17:51:11.925151image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

ablösevereinbarung
Real number (ℝ≥0)

ZEROS

Distinct count159
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.26863620748883
Minimum0.0
Maximum2950.0
Zeros12613
Zeros (%)86.7%
Memory size113.7 KiB
2020-08-12T17:51:12.089822image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile350
Maximum2950
Range2950
Interquartile range (IQR)0

Descriptive statistics

Standard deviation167.5036223
Coefficient of variation (CV)3.543652531
Kurtosis40.67654652
Mean47.26863621
Median Absolute Deviation (MAD)0
Skewness5.437243088
Sum687995
Variance28057.46349
2020-08-12T17:51:12.440868image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01261386.7%
 
2001741.2%
 
1501451.0%
 
1001401.0%
 
3001380.9%
 
5001280.9%
 
4001250.9%
 
2501140.8%
 
501010.7%
 
350690.5%
 
Other values (149)8085.6%
 
ValueCountFrequency (%) 
01261386.7%
 
15< 0.1%
 
21< 0.1%
 
41< 0.1%
 
52< 0.1%
 
ValueCountFrequency (%) 
29501< 0.1%
 
27001< 0.1%
 
21501< 0.1%
 
20002< 0.1%
 
19001< 0.1%
 

aufzug
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
12596
1
 
1959
ValueCountFrequency (%) 
01259686.5%
 
1195913.5%
 

azubi_wg
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
14234
1
 
321
ValueCountFrequency (%) 
01423497.8%
 
13212.2%
 

badewanne
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
1
8483
0
6072
ValueCountFrequency (%) 
1848358.3%
 
0607241.7%
 

balkon
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
9373
1
5182
ValueCountFrequency (%) 
0937364.4%
 
1518235.6%
 

bed
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
möbliert
7390
no_info_or_rare
4551
teilmöbliert
2614
ValueCountFrequency (%) 
möbliert739050.8%
 
no_info_or_rare455131.3%
 
teilmöbliert261418.0%
 
2020-08-12T17:51:12.942516image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length15
Median length8
Mean length10.90711096
Min length8

beruf_wg
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
1
8916
0
5639
ValueCountFrequency (%) 
1891661.3%
 
0563938.7%
 

buildings
Categorical

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
mehrfamilienhaus
3801
altbau
3155
sanierter altbau
2742
no_info_or_rare
2271
neubau
1267
Other values (5)
1319
ValueCountFrequency (%) 
mehrfamilienhaus380126.1%
 
altbau315521.7%
 
sanierter altbau274218.8%
 
no_info_or_rare227115.6%
 
neubau12678.7%
 
reihenhaus4433.0%
 
hochhaus3562.4%
 
einfamilienhaus3212.2%
 
doppelhaus1491.0%
 
plattenbau500.3%
 
2020-08-12T17:51:13.210879image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length16
Median length15
Mean length12.32346273
Min length6

bus
Real number (ℝ≥0)

Distinct count18
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.261216076949502
Minimum1.0
Maximum38.0
Zeros0
Zeros (%)0.0%
Memory size113.7 KiB
2020-08-12T17:51:13.352534image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum38
Range37
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.105974417
Coefficient of variation (CV)0.6457635334
Kurtosis9.311718751
Mean3.261216077
Median Absolute Deviation (MAD)1
Skewness1.829707039
Sum47467
Variance4.435128245
2020-08-12T17:51:13.510109image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3400927.5%
 
1302920.8%
 
5281219.3%
 
2280119.2%
 
46854.7%
 
73782.6%
 
103642.5%
 
82191.5%
 
62051.4%
 
15230.2%
 
Other values (8)300.2%
 
ValueCountFrequency (%) 
1302920.8%
 
2280119.2%
 
3400927.5%
 
46854.7%
 
5281219.3%
 
ValueCountFrequency (%) 
381< 0.1%
 
251< 0.1%
 
231< 0.1%
 
201< 0.1%
 
15230.2%
 

car
Categorical

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
gute parkmöglichkeiten
5862
no_info_or_rare
4101
schlechte parkmöglichkeiten
2025
bewohnerparken
1992
eigener parkplatz
 
324
ValueCountFrequency (%) 
gute parkmöglichkeiten586240.3%
 
no_info_or_rare410128.2%
 
schlechte parkmöglichkeiten202513.9%
 
bewohnerparken199213.7%
 
eigener parkplatz3242.2%
 
tiefgaragenstellplatz2511.7%
 
2020-08-12T17:51:13.793566image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length27
Median length22
Mean length19.49989694
Min length14

dauer
Real number (ℝ≥0)

ZEROS

Distinct count173
Unique (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.350463758158709
Minimum0.0
Maximum1418.0
Zeros14107
Zeros (%)96.9%
Memory size113.7 KiB
2020-08-12T17:51:13.994576image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1418
Range1418
Interquartile range (IQR)0

Descriptive statistics

Standard deviation49.86858592
Coefficient of variation (CV)7.852747109
Kurtosis203.6242921
Mean6.350463758
Median Absolute Deviation (MAD)0
Skewness12.52733741
Sum92431
Variance2486.875861
2020-08-12T17:51:14.175126image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01410796.9%
 
91230.2%
 
60190.1%
 
152150.1%
 
183140.1%
 
244140.1%
 
121140.1%
 
92120.1%
 
30100.1%
 
153100.1%
 
Other values (163)3172.2%
 
ValueCountFrequency (%) 
01410796.9%
 
11< 0.1%
 
101< 0.1%
 
153< 0.1%
 
161< 0.1%
 
ValueCountFrequency (%) 
14181< 0.1%
 
9963< 0.1%
 
9951< 0.1%
 
9741< 0.1%
 
9432< 0.1%
 

dielen
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
13447
1
 
1108
ValueCountFrequency (%) 
01344792.4%
 
111087.6%
 

dusche
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
1
8634
0
5921
ValueCountFrequency (%) 
1863459.3%
 
0592140.7%
 

fahrradkeller
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
9694
1
4861
ValueCountFrequency (%) 
0969466.6%
 
1486133.4%
 

fire
Categorical

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
no_info_or_rare
7549
zentralheizung
4181
gasheizung
2448
fernwärme
 
268
nachtspeicherofen
 
79
Other values (2)
 
30
ValueCountFrequency (%) 
no_info_or_rare754951.9%
 
zentralheizung418128.7%
 
gasheizung244816.8%
 
fernwärme2681.8%
 
nachtspeicherofen790.5%
 
ofenheizung290.2%
 
kohleofen1< 0.1%
 
2020-08-12T17:51:14.453416image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length17
Median length15
Mean length13.76379251
Min length9

fliesen
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
11000
1
3555
ValueCountFrequency (%) 
01100075.6%
 
1355524.4%
 

frauen_wg
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
12773
1
 
1782
ValueCountFrequency (%) 
01277387.8%
 
1178212.2%
 

fußbodenheizung
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
13984
1
 
571
ValueCountFrequency (%) 
01398496.1%
 
15713.9%
 

garten
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
12833
1
 
1722
ValueCountFrequency (%) 
01283388.2%
 
1172211.8%
 

gartenmitbenutzung
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
11877
1
2678
ValueCountFrequency (%) 
01187781.6%
 
1267818.4%
 

gemischt_wg
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
7493
1
7062
ValueCountFrequency (%) 
0749351.5%
 
1706248.5%
 

gesamtmiete
Real number (ℝ≥0)

Distinct count567
Unique (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494.67894194434905
Minimum155.0
Maximum1120.0
Zeros0
Zeros (%)0.0%
Memory size113.7 KiB
2020-08-12T17:51:14.617946image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum155
5-th percentile300
Q1400
median485
Q3570
95-th percentile730
Maximum1120
Range965
Interquartile range (IQR)170

Descriptive statistics

Standard deviation130.2805201
Coefficient of variation (CV)0.2633637883
Kurtosis0.8601147831
Mean494.6789419
Median Absolute Deviation (MAD)85
Skewness0.6393734944
Sum7200052
Variance16973.01393
2020-08-12T17:51:14.746655image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5008736.0%
 
4508686.0%
 
5507565.2%
 
4007054.8%
 
6005353.7%
 
6504062.8%
 
3503592.5%
 
4802751.9%
 
4302491.7%
 
3802411.7%
 
Other values (557)928863.8%
 
ValueCountFrequency (%) 
1551< 0.1%
 
1703< 0.1%
 
1753< 0.1%
 
1781< 0.1%
 
180110.1%
 
ValueCountFrequency (%) 
11201< 0.1%
 
11005< 0.1%
 
10831< 0.1%
 
10751< 0.1%
 
10571< 0.1%
 

haustiere
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
13943
1
 
612
ValueCountFrequency (%) 
01394395.8%
 
16124.2%
 

kabel
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
10608
1
3947
ValueCountFrequency (%) 
01060872.9%
 
1394727.1%
 

keine_zweck_wg
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
8892
1
5663
ValueCountFrequency (%) 
0889261.1%
 
1566338.9%
 

keller
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
7527
1
7028
ValueCountFrequency (%) 
0752751.7%
 
1702848.3%
 

laminat
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
9249
1
5306
ValueCountFrequency (%) 
0924963.5%
 
1530636.5%
 

m2_pro_pers
Real number (ℝ≥0)

Distinct count369
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.69129484674375
Minimum0.7058823529411765
Maximum333.0
Zeros0
Zeros (%)0.0%
Memory size113.7 KiB
2020-08-12T17:51:14.894271image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.7058823529
5-th percentile20.66666667
Q126.66666667
median30
Q332.5
95-th percentile45
Maximum333
Range332.2941176
Interquartile range (IQR)5.833333333

Descriptive statistics

Standard deviation8.382557832
Coefficient of variation (CV)0.2731249325
Kurtosis139.1267306
Mean30.69129485
Median Absolute Deviation (MAD)3.333333333
Skewness5.758894354
Sum446711.7965
Variance70.26727581
2020-08-12T17:51:15.054580image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
30506734.8%
 
257084.9%
 
354693.2%
 
27.54082.8%
 
26.666666673862.7%
 
23.333333333712.5%
 
403592.5%
 
32.53362.3%
 
33.333333333152.2%
 
202591.8%
 
Other values (359)587740.4%
 
ValueCountFrequency (%) 
0.70588235291< 0.1%
 
0.751< 0.1%
 
0.8751< 0.1%
 
1.6251< 0.1%
 
1.71< 0.1%
 
ValueCountFrequency (%) 
3331< 0.1%
 
166.51< 0.1%
 
1601< 0.1%
 
157.51< 0.1%
 
1501< 0.1%
 

parkett
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
10781
1
3774
ValueCountFrequency (%) 
01078174.1%
 
1377425.9%
 

personen
Real number (ℝ≥0)

Distinct count16
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.147097217451048
Minimum2.0
Maximum17.0
Zeros0
Zeros (%)0.0%
Memory size113.7 KiB
2020-08-12T17:51:15.224012image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum17
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.478304112
Coefficient of variation (CV)0.4697357627
Kurtosis22.1881135
Mean3.147097217
Median Absolute Deviation (MAD)1
Skewness3.617902676
Sum45806
Variance2.185383046
2020-08-12T17:51:15.417492image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3576639.6%
 
2505234.7%
 
4235716.2%
 
56264.3%
 
63472.4%
 
71330.9%
 
81120.8%
 
10370.3%
 
9270.2%
 
11270.2%
 
Other values (6)710.5%
 
ValueCountFrequency (%) 
2505234.7%
 
3576639.6%
 
4235716.2%
 
56264.3%
 
63472.4%
 
ValueCountFrequency (%) 
17160.1%
 
16170.1%
 
156< 0.1%
 
144< 0.1%
 
131< 0.1%
 

pvc
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
13522
1
 
1033
ValueCountFrequency (%) 
01352292.9%
 
110337.1%
 

rauchen
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
keine_angabe
13867
raucher
 
665
nichtraucher
 
23
ValueCountFrequency (%) 
keine_angabe1386795.3%
 
raucher6654.6%
 
nichtraucher230.2%
 
2020-08-12T17:51:15.921148image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.77155617
Min length7

satellit
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
13605
1
 
950
ValueCountFrequency (%) 
01360593.5%
 
19506.5%
 

spülmaschine
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
1
8260
0
6295
ValueCountFrequency (%) 
1826056.8%
 
0629543.2%
 

status
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
inaktiv
13161
unbefristet
 
946
befristet
 
448
ValueCountFrequency (%) 
inaktiv1316190.4%
 
unbefristet9466.5%
 
befristet4483.1%
 
2020-08-12T17:51:16.219383image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.32153899
Min length7

stock
Categorical

Distinct count12
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
1
3021
2
2844
3
2267
4
1585
eg
1524
Other values (7)
3314
ValueCountFrequency (%) 
1302120.8%
 
2284419.5%
 
3226715.6%
 
4158510.9%
 
eg152410.5%
 
no_info_or_rare13989.6%
 
55834.0%
 
hochparterre5163.5%
 
dachgeschoss4353.0%
 
höher als 52852.0%
 
Other values (2)970.7%
 
2020-08-12T17:51:16.479189image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length15
Median length1
Mean length3.42322226
Min length1

straße
Categorical

HIGH CARDINALITY

Distinct count338
Unique (%)2.3%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
no_info_or_rare
5926
eschersheimerlandstrasse
 
271
launhardtstrasse
 
139
bergerstrasse
 
111
mainzerlandstrasse
 
109
Other values (333)
7999
ValueCountFrequency (%) 
no_info_or_rare592640.7%
 
eschersheimerlandstrasse2711.9%
 
launhardtstrasse1391.0%
 
bergerstrasse1110.8%
 
mainzerlandstrasse1090.7%
 
siolistrasse1090.7%
 
mörfelderlandstrasse1070.7%
 
friedbergerlandstrasse1060.7%
 
münchenerstrasse980.7%
 
eckenheimerlandstrasse960.7%
 
Other values (328)748351.4%
 
2020-08-12T17:51:16.754928image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length33
Median length15
Mean length15.11150807
Min length3

studenten_wg
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
1
7404
0
7151
ValueCountFrequency (%) 
1740450.9%
 
0715149.1%
 

teppich
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
13846
1
 
709
ValueCountFrequency (%) 
01384695.1%
 
17094.9%
 

terrasse
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
13199
1
 
1356
ValueCountFrequency (%) 
01319990.7%
 
113569.3%
 

waschmaschine
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
1
11513
0
3042
ValueCountFrequency (%) 
11151379.1%
 
0304220.9%
 

wohnung
Real number (ℝ≥0)

Distinct count203
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.16200618344212
Minimum12.0
Maximum999.0
Zeros0
Zeros (%)0.0%
Memory size113.7 KiB
2020-08-12T17:51:17.004740image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile54
Q170
median80
Q390
95-th percentile140
Maximum999
Range987
Interquartile range (IQR)20

Descriptive statistics

Standard deviation32.03465811
Coefficient of variation (CV)0.3717956386
Kurtosis86.81683989
Mean86.16200618
Median Absolute Deviation (MAD)10
Skewness5.57686201
Sum1254088
Variance1026.21932
2020-08-12T17:51:17.172802image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
80479933.0%
 
707355.0%
 
1006094.2%
 
605643.9%
 
905333.7%
 
654713.2%
 
754683.2%
 
1203282.3%
 
853202.2%
 
1103102.1%
 
Other values (193)541837.2%
 
ValueCountFrequency (%) 
123< 0.1%
 
131< 0.1%
 
141< 0.1%
 
151< 0.1%
 
165< 0.1%
 
ValueCountFrequency (%) 
9991< 0.1%
 
7002< 0.1%
 
6661< 0.1%
 
5801< 0.1%
 
4501< 0.1%
 

zimmergröße
Real number (ℝ≥0)

Distinct count47
Unique (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.44177258673995
Minimum7.0
Maximum59.0
Zeros0
Zeros (%)0.0%
Memory size113.7 KiB
2020-08-12T17:51:17.372270image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile11
Q114
median16
Q320
95-th percentile26
Maximum59
Range52
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.152540544
Coefficient of variation (CV)0.2954138129
Kurtosis6.71636351
Mean17.44177259
Median Absolute Deviation (MAD)3
Skewness1.760889483
Sum253865
Variance26.54867406
2020-08-12T17:51:17.584792image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
15176012.1%
 
20175012.0%
 
16163811.3%
 
1814019.6%
 
1411968.2%
 
1710707.4%
 
1210106.9%
 
137565.2%
 
255433.7%
 
224853.3%
 
Other values (37)294620.2%
 
ValueCountFrequency (%) 
7160.1%
 
8470.3%
 
9970.7%
 
104463.1%
 
113692.5%
 
ValueCountFrequency (%) 
591< 0.1%
 
557< 0.1%
 
531< 0.1%
 
523< 0.1%
 
511< 0.1%
 

zweck_wg
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.7 KiB
0
7465
1
7090
ValueCountFrequency (%) 
0746551.3%
 
1709048.7%
 

Interactions

2020-08-12T17:50:54.698603image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:54.878111image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:55.026740image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:55.170362image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:55.416669image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:55.588211image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:55.759793image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:55.939314image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:56.134856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:56.285450image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:56.432100image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:56.587704image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:56.722333image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:56.862958image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:57.057096image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:57.247242image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:57.416790image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:57.581382image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:57.762862image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:57.947392image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:58.092025image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:58.265146image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:58.466707image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:58.683699image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:58.905108image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:59.237316image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:59.452840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:59.619394image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:59.787409image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:50:59.986384image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:00.302541image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:00.510537image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:00.689063image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:00.881549image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:01.316983image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:01.520557image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:01.725047image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:01.953942image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:02.161387image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:02.333927image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:02.511488image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:02.699976image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:02.915135image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:03.136543image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:03.334536image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:03.515053image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:03.795331image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:04.116454image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:04.313962image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:04.526043image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:04.743412image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:04.963840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:05.173692image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:05.346260image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:05.529856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:05.700407image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:05.891870image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:06.107240image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:06.298733image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:06.479316image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:06.643906image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:06.814992image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:07.016989image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:07.197541image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-08-12T17:51:17.818214image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-12T17:51:18.534837image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-12T17:51:19.252092image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-12T17:51:20.039949image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-12T17:51:20.813206image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-12T17:51:08.010484image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-12T17:51:10.820812image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

PLZablösevereinbarungaufzugazubi_wgbadewannebalkonbedberuf_wgbuildingsbuscardauerdielenduschefahrradkellerfirefliesenfrauen_wgfußbodenheizunggartengartenmitbenutzunggemischt_wggesamtmietehaustierekabelkeine_zweck_wgkellerlaminatm2_pro_persparkettpersonenpvcrauchensatellitspülmaschinestatusstockstraßestudenten_wgteppichterrassewaschmaschinewohnungzimmergrößezweck_wg
0603270.01000teilmöbliert1mehrfamilienhaus5.0no_info_or_rare0.0010zentralheizung000001540.01001121.66666706.00keine_angabe00inaktiv3no_info_or_rare0001130.020.00
1603260.00000no_info_or_rare1no_info_or_rare5.0no_info_or_rare0.0000no_info_or_rare000001600.00010025.00000003.00keine_angabe00inaktivno_info_or_rareidsteinerstrasse000075.014.01
2659310.00010no_info_or_rare1mehrfamilienhaus1.0gute parkmöglichkeiten0.0010no_info_or_rare110111600.01011161.50000002.00keine_angabe11unbefristet2no_info_or_rare0000123.013.01
3659310.00001teilmöbliert0sanierter altbau10.0gute parkmöglichkeiten0.0010gasheizung000000600.00101155.00000002.00keine_angabe01inaktiv1no_info_or_rare0001110.040.01
4659310.00000no_info_or_rare1einfamilienhaus3.0no_info_or_rare0.0010zentralheizung100111630.00001050.00000014.00keine_angabe11inaktivno_info_or_rareno_info_or_rare0011200.030.00
5659310.00000teilmöbliert1einfamilienhaus3.0no_info_or_rare0.0000no_info_or_rare100101570.00000050.00000014.00keine_angabe10unbefristet1no_info_or_rare0000200.016.00
6659310.00001teilmöbliert1mehrfamilienhaus5.0gute parkmöglichkeiten0.0101no_info_or_rare110001400.00011140.00000002.00keine_angabe01inaktivegno_info_or_rare110180.020.01
7659310.00000no_info_or_rare1altbau3.0no_info_or_rare0.0000no_info_or_rare000000470.00000025.00000002.00keine_angabe00inaktiv1no_info_or_rare000050.023.00
8659310.00000no_info_or_rare1reihenhaus10.0no_info_or_rare0.0101no_info_or_rare100101690.00011036.66666703.00keine_angabe00inaktivno_info_or_rareno_info_or_rare0010110.018.01
9659310.00010möbliert0einfamilienhaus3.0gute parkmöglichkeiten0.0010zentralheizung100110720.00011050.00000014.00keine_angabe11inaktiv2no_info_or_rare0011200.035.01

Last rows

PLZablösevereinbarungaufzugazubi_wgbadewannebalkonbedberuf_wgbuildingsbuscardauerdielenduschefahrradkellerfirefliesenfrauen_wgfußbodenheizunggartengartenmitbenutzunggemischt_wggesamtmietehaustierekabelkeine_zweck_wgkellerlaminatm2_pro_persparkettpersonenpvcrauchensatellitspülmaschinestatusstockstraßestudenten_wgteppichterrassewaschmaschinewohnungzimmergrößezweck_wg
14545999990.00000teilmöbliert1mehrfamilienhaus3.0gute parkmöglichkeiten0.0010zentralheizung110000290.00001021.33333313.00keine_angabe00inaktiv1no_info_or_rare101164.013.00
14546605960.01010möbliert1neubau1.0no_info_or_rare0.0011no_info_or_rare000000764.00101023.33333303.00keine_angabe01inaktiv1no_info_or_rare000170.014.00
14547605290.00011möbliert1einfamilienhaus2.0gute parkmöglichkeiten0.0010zentralheizung100111579.00000130.00000015.00keine_angabe01inaktiv1no_info_or_rare0011150.022.00
14548603851000.00001möbliert0sanierter altbau5.0schlechte parkmöglichkeiten0.0010no_info_or_rare010010450.00001126.00000003.00keine_angabe01inaktiv1no_info_or_rare000178.012.00
14549603180.00001möbliert1sanierter altbau5.0no_info_or_rare0.0010gasheizung000001600.00110035.00000012.00keine_angabe01inaktiv1no_info_or_rare000170.017.01
14550999990.00010teilmöbliert1mehrfamilienhaus5.0gute parkmöglichkeiten0.0111zentralheizung000111390.00001057.50000002.00keine_angabe11inaktiv1no_info_or_rare1001115.024.00
14551605960.00010no_info_or_rare1sanierter altbau1.0bewohnerparken0.0011no_info_or_rare010001625.01111146.00000002.00keine_angabe01inaktivdachgeschosscranachstrasse100092.012.01
14552605980.00000no_info_or_rare1no_info_or_rare1.0no_info_or_rare0.0000no_info_or_rare000001730.00000028.00000004.00keine_angabe00inaktivhöher als 5no_info_or_rare1000112.017.00
14553999990.00001möbliert1mehrfamilienhaus2.0gute parkmöglichkeiten91.0011no_info_or_rare000000590.00001033.33333313.00keine_angabe01befristetdachgeschossno_info_or_rare0001100.020.00
14554659290.00000möbliert1neubau5.0gute parkmöglichkeiten0.0011zentralheizung101000530.00010032.00000003.00keine_angabe11inaktiv1no_info_or_rare000196.020.01

Duplicate rows

Most frequent

PLZablösevereinbarungaufzugazubi_wgbadewannebalkonbedberuf_wgbuildingsbuscardauerdielenduschefahrradkellerfirefliesenfrauen_wgfußbodenheizunggartengartenmitbenutzunggemischt_wggesamtmietehaustierekabelkeine_zweck_wgkellerlaminatm2_pro_persparkettpersonenpvcrauchensatellitspülmaschinestatusstockstraßestudenten_wgteppichterrassewaschmaschinewohnungzimmergrößezweck_wgcount
15603140.00010möbliert1mehrfamilienhaus2.0no_info_or_rare0.0010gasheizung100010590.00000123.75000014.00keine_angabe01inaktiveglaunhardtstrasse000195.017.0051
17603140.00011möbliert0mehrfamilienhaus2.0bewohnerparken0.0011gasheizung100011510.00000023.75000014.00keine_angabe01inaktiveglaunhardtstrasse000195.017.0024
11603140.00010möbliert0mehrfamilienhaus2.0no_info_or_rare0.0010gasheizung100011620.00000123.75000014.00keine_angabe01inaktiveglaunhardtstrasse000195.017.0016
9603140.00010möbliert0mehrfamilienhaus2.0no_info_or_rare0.0010gasheizung100011590.00000123.75000014.00keine_angabe01inaktiveglaunhardtstrasse000195.017.007
71604860.00010möbliert1mehrfamilienhaus3.0gute parkmöglichkeiten0.0011zentralheizung000000525.00000136.00000002.00keine_angabe01inaktivegno_info_or_rare000172.015.005
107999990.00000no_info_or_rare1no_info_or_rare3.0no_info_or_rare0.0000no_info_or_rare000000500.00000030.00000004.00keine_angabe00inaktivno_info_or_rareno_info_or_rare000080.020.005
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14603140.00010möbliert1mehrfamilienhaus2.0no_info_or_rare0.0010gasheizung100010590.00000023.75000014.00keine_angabe01inaktiveglaunhardtstrasse000195.017.004
37603260.00010möbliert0sanierter altbau2.0gute parkmöglichkeiten0.0010zentralheizung000000500.00100023.33333303.01keine_angabe01inaktivegno_info_or_rare100170.015.004
4603130.00011möbliert1mehrfamilienhaus3.0gute parkmöglichkeiten0.0010gasheizung100001550.00100127.50000002.00keine_angabe01inaktiv1allerheiligenstrasse100155.020.013